CN113627527A - Non-standard detection equipment monitoring system - Google Patents
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Abstract
The invention provides a non-standard detection equipment monitoring system, comprising: edge equipment: configuring a monitoring program, analyzing the operation state of each non-standard detection device based on the collected operation and detection data, and assisting the non-standard device data processing; the data cooperation device: communicating with each edge device node, and cooperatively scheduling the computing power of the edge device to realize data processing optimization; the remote gateway: communicating with the data coordination equipment, acquiring the running state of each non-standard detection equipment, and sending the control information and the update information of the edge equipment node to the edge equipment node through the data coordination equipment; the non-standard detection equipment is communicated with the remote gateway and the tested piece so as to send the detected information of the tested piece to the remote gateway. An embedded intelligent AI module (edge device) is added in a system frame, and the health state and the performance of non-standard detection equipment of the equipment can be judged by analyzing the data of the equipment and the test data of a tested piece.
Description
Technical Field
The invention relates to the technical field of railway vehicles, in particular to a non-standard detection equipment monitoring system.
Background
The rail transit non-standard detection equipment has the characteristic of strong individuation, and the manual maintenance cost is increased. In the process of digital transformation in the rail transit industry, the support is realized by networking, and higher requirements are provided in the aspects of reducing the maintenance cost of non-standard detection equipment, realizing remote maintenance and the like.
In the prior art, a maintenance method and characteristics of non-standard detection equipment are as follows.
The first common method comprises the following steps: referring to fig. 1, an internet gateway is added to a non-standard detection device, so that device data can be uploaded to a related common cloud or private cloud, and then the next processing is performed. In this method, the gateway mainly plays the role of uploading and issuing, so that the device can be managed at the remote end.
A second common method comprises the following steps: referring to fig. 2, the internet of things gateway is adopted to preprocess the original data of the device, and then upload the processed data to the common cloud or the private cloud for further processing. The method comprises the steps of firstly carrying out preprocessing and calculation such as data cleaning and data slicing on original data of equipment in a gateway, and then uploading data with higher value to a cloud for further processing.
Two methods commonly used in the prior art can realize the networking of the device and the uploading of data, but both methods need to modify the software architecture inside the device. For non-standard detection devices, the devices are different, and the internal software architecture and data flow are different. In the processing mode in the prior art, the thread is started again on the original measurement and control software program, the data packet is captured, the data is packaged, and then the data is uploaded to the Internet of things. Each non-standard detection device needs to be deeply developed, and the processing efficiency is low.
Aiming at the characteristics that non-standard detection equipment and products in the rail transit industry have three-high one-scattered, namely, the products are high in number of types, high in monomer value, high in operation and maintenance cost and distributed and scattered in the whole country, a general framework is urgently needed to solve the problem of how to connect isolated non-standard equipment into a digital network, interconnection and intercommunication among the equipment are realized, and the control of the whole life cycle is provided for the maintenance and overhaul of the whole vehicle. By deploying the data coordination device, the relevant parameters of various types of devices distributed all over the country can be remotely observed, and meanwhile, the non-mapping device can be maintained.
Disclosure of Invention
The present invention is to solve one of the above technical problems, and provides a non-standard detection device monitoring system based on edge calculation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a non-standard detection device monitoring system, comprising:
edge equipment: the system comprises a plurality of edge equipment nodes, wherein each edge equipment node can at least communicate with a type of non-standard detection equipment to acquire the running state information and detection data of the type of non-standard detection equipment; each type of non-standard detection equipment corresponds to at least one type of communication interface; the operation state of each non-standard detection device can be analyzed based on the collected operation data, and the data processing of the non-standard devices is assisted based on the detection data of the non-standard devices;
the data cooperation device: communicating with each edge device node, distributing cloud server data, and cooperatively scheduling the computing capacity of the edge device to realize data processing optimization;
the remote gateway: the data coordination equipment is connected with the data coordination equipment, communicates with the cloud server, and sends the control information and the update information of the edge equipment node to the edge equipment node through the data coordination equipment;
the non-standard detection equipment is communicated with the remote gateway and the tested piece so as to send the detected information of the tested piece to the remote gateway and collect the detection data of the tested piece.
In some embodiments of the present invention, the data coordination device is configured at an edge device node connected to the non-standard detection device, and when there is a new operation computation demand, distributes data required by the new operation computation demand to an idle edge device node to perform operation computation, thereby implementing data processing optimization.
In some embodiments of the present invention, the edge device node is configured with:
and (3) a state monitoring algorithm: acquiring data of non-standard detection equipment, and analyzing the running state of the non-standard detection equipment based on the data;
an expert system algorithm: and acquiring sensor data, and performing data analysis on detection data of the non-standard detection equipment to analyze the performance of the tested piece.
In some embodiments of the invention, the non-standard detection equipment comprises an industrial personal computer, the edge equipment comprises edge equipment nodes based on TCP/IP, the edge equipment nodes are communicated with the industrial personal computer and a sensor, and the sensor is connected to a tested piece;
the data required by the state monitoring algorithm of the industrial personal computer and the algorithm of the expert system can be directly acquired by the sensor information, or data twinning is carried out by the industrial personal computer to acquire the data of the industrial personal computer.
In some embodiments of the present invention, the execution flow of the expert system algorithm includes:
a data preprocessing step: the method comprises the steps of data slicing processing, filtering processing, resampling processing and normalized sampling frequency processing;
a characteristic extraction step: determining normal key features and fault key features based on statistical information of the normal operation data and the fault operation data;
an algorithm training step: performing algorithm training based on the training model, the preprocessed data and the feature extraction data;
the algorithm pre-verification step: and comparing the detected data of the tested piece with the generated data of the algorithm to verify the accuracy of the algorithm.
In some embodiments of the invention, the expert system algorithm is further configured to:
performing data preprocessing and feature extraction based on historical data;
and performing fault data injection based on twin data, and performing algorithm training by combining the data after feature extraction.
In some embodiments of the present invention, the non-standard detection device includes an image acquisition device, and when the edge device node is an image monitoring edge device, the expert system algorithm is configured to perform image processing algorithms such as image denoising and image recognition according to image acquisition data.
In some embodiments of the present invention, the edge device further includes an edge device node based on serial communication modes such as RS485 and RS232, an edge device node based on USB, and an edge device node based on GPIB.
Compared with the prior art, the invention has the technical advantages that:
an embedded intelligent AI module (edge device) is added in a system frame, and the health state and the performance of non-standard detection equipment of the equipment can be judged by analyzing the data of the equipment and the test data of a tested piece. The framework enables the edge calculation target of the non-standard detection equipment to be realized without carrying out a large amount of reconstruction on the existing sizing equipment.
The method solves the problem that network congestion is easily caused because a large amount of process data and test data generated by non-standard detection equipment are uploaded to a cloud server in the using process; meanwhile, the networking requirements of non-standard equipment are met, the manual maintenance cost of the existing non-standard detection equipment is reduced, and the requirement of remote maintenance is met; the method provides better service while supplying the equipment, and provides a detailed solution for realizing the traditional supply mode to the supply mode of equipment + service.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of a first embodiment of a non-standard detection device monitoring system in the prior art;
FIG. 2 is a schematic diagram of a monitoring system of a second embodiment of a non-standard detection device in the prior art;
FIG. 3 is a schematic diagram of a non-standard detection device monitoring system of the present invention;
FIG. 4 is a diagram of the monitoring system architecture of the non-standard inspection equipment of the present invention;
FIG. 5 is a diagram of the monitoring system architecture of the non-standard inspection equipment of the present invention;
FIG. 6 is a diagram of the monitoring system architecture of the non-standard inspection equipment of the present invention;
FIG. 7 is a diagram of the monitoring system architecture of the non-standard inspection equipment of the present invention;
FIG. 8 is an expert algorithm diagnostic flow chart;
FIG. 9 is a flow chart of expert algorithm data preprocessing and feature extraction;
FIG. 10 is a flow chart of expert algorithm training.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a monitoring system of non-standard detection equipment, which can be used for monitoring the non-standard detection equipment in the technical field of railway vehicles.
Referring to fig. 3, the non-standard detection device monitoring system includes: edge device, data distribution system device, remote gateway.
The non-standard detection equipment is connected with the sensor, the execution mechanism, the monitoring equipment and the like.
Edge equipment: the method comprises the following steps that a plurality of edge equipment nodes are included, and each edge equipment node can be communicated with a type of non-standard detection equipment to acquire operation data of the type of non-standard detection equipment; each type of non-standard detection equipment corresponds to at least one type of communication interface; the edge device node is provided with a monitoring program, and based on the collected operation data, the operation state of each non-standard detection device can be specifically processed or analyzed.
The method is adaptive to the classification of nonstandard detection equipment, and the edge equipment comprises an Ethernet edge equipment node based on TCP/IP, a serial port edge equipment node based on RS485 and RS232, an edge equipment node based on USB and an edge equipment node based on GPIB. Based on abundant interface design, the edge device supports various hardware interfaces and can be downward compatible with interfaces of non-standard detection equipment of an industrial personal computer, a PLC (programmable logic controller), various embedded devices and other main controllers; an interface in the form of a wired or wireless connection to an upward compatible data coordinating device.
The data cooperation device: and communicating with each equipment node to acquire the operation data and the operation state information of each non-standard detection equipment.
The remote gateway: and communicating with the data coordination equipment, acquiring the running state of each non-standard detection equipment, and sending the control information and the update information of the edge equipment node to the edge equipment node through the data coordination equipment.
The nonstandard detection equipment is communicated with the remote gateway and the tested piece through the data cooperation equipment so as to send the detected information of the tested piece to the remote gateway.
The invention defines two interfaces: a north interface: an interface provided for other manufacturers or operators (data coordination equipment) to access and manage, namely an interface provided upwards; a southbound interface: the interface of the non-standard detection equipment is independently developed by managing other manufacturer equipment or the company, namely, the interface is provided downwards.
A southbound interface: and with the gradual increase of equipment of each large vehicle section or motor train station in the rail transit industry, the network scale of subordinate equipment is continuously enlarged, the number of nonstandard equipment in the network is increased, and higher requirements and challenges are provided for the southbound interface of the edge equipment. And at the same time, the northbound interface does not need to be greatly modified with the increase of equipment. An interface (southward interface) downward arranged on the intelligent gateway needs to consider the complexity of a communication connection mode of terminal equipment in each industry, and needs to have rich interface/protocol capability so as to be applied to wider industry markets; at present, for the above analysis, at least mainstream communication protocols such as TCP/IP, DF1, OPC, SNP, MPI, MODBUS/PLUS, HostLink, ControlLink, CC-LINK, MEWTOCOL, etc. need to be covered, communication media of these protocols are basically RJ45, serial ports, USB, etc., there is compatibility in physical structure and appearance, and specific protocol contents can be configured in the software layer of the intelligent edge device.
A north interface: the northbound interface has certain universality, so that an OPC UA unified architecture is selected in numerous interface specifications to support complex data built-in, unified address space, cross-platform operation and abstract service functions. The functions can be used as an application program such as a pure OPC UA client tool alone or integrated into a user program such as an MRS system supporting the OPC UA client function, and the user program may also centralize the functions of the server and the client. The invention provides an OPC UA unified architecture of an edge device in edge calculation, wherein the edge device is used as a service end of the OPC UA, and other devices on the upper layer (northbound) of the edge device are used as clients of the OPC UA.
In order to improve the monitoring efficiency, in some embodiments of the present invention, the data coordination device is configured to, when the edge device node connected to the non-standard detection device performs the running computation and has a new running computation demand, distribute data required by the new running computation demand to the idle edge device node to perform the running computation. Specifically, the data coordination device may receive and manage data of the plurality of non-standard detection devices, and perform coordination management on the data, and when the data amount is large and the operation is complex, the data coordination device may call other idle edge node devices to perform processing.
Meanwhile, the data distribution and coordination device serves as a bridge between the gateway and the edge node device, and the remote data end can update the data processing model in the edge computing device through the data coordination device.
In some embodiments of the present invention, the edge device node has a function of monitoring a state of the non-standard detection device, and may also perform a part of functions of the non-standard detection device, and specifically, the edge device node is configured with:
and (3) a state monitoring algorithm: acquiring data of non-standard detection equipment, and analyzing the running state of the non-standard detection equipment based on the data;
an expert system algorithm: and acquiring sensor data, and performing data analysis on detection data of the non-standard detection equipment to analyze the performance of the tested piece.
The following describes a specific monitoring procedure of the edge node according to the present invention with reference to several specific non-standard detection devices.
In some embodiments of the invention, the non-standard detection device comprises an industrial personal computer, such as a porphyry industrial personal computer, an NI industrial personal computer; the edge device comprises an edge device node based on TCP/IP, and is communicated with the industrial personal computer and the sensor, and the sensor is connected to the tested piece so as to collect the data of the tested piece;
the data required by the state monitoring algorithm of the industrial personal computer and the algorithm of the expert system can be directly acquired by the sensor information, or data twinning is carried out by the industrial personal computer to acquire the data of the industrial personal computer.
Aiming at an industrial personal computer, an edge node (equipment) adopts an embedded hardware system, wherein two sets of algorithms are deployed: the test bed state monitoring algorithm of the industrial personal computer and the expert system algorithm of the industrial personal computer are connected with the tested piece. The input of the two sets of algorithms can be realized in two ways: the data are directly acquired from the sensor or twin data are sent to the edge nodes through the Ethernet by using the industrial personal computer, the edge nodes perform a large amount of complex operations on the data, and the high-performance storage and operation of the edge nodes are used, so that the data are analyzed to realize the function of the tested piece expert system under the condition of not influencing the original detection efficiency, and the self state detection of a non-standard system can be realized to realize the state monitoring algorithm of the test bed. The difference between the two modes lies in the requirement of real-time performance, and if the condition monitoring of some test beds needs faster real-time performance, the data needs to be directly read from the sensor so as to obtain higher sampling rate and synchronism.
The invention provides an edge computing framework of digital-analog signal non-standard equipment based on an industrial personal computer, which utilizes a hardware interface and data twin of the industrial personal computer to transmit detection data of a test bed or test bed state data to edge equipment, realizes the edge computing capability of big data and complex algorithm on the premise of basically not influencing the functions and the performances of the original test bed through the strong computing power of the edge equipment, and meanwhile, the edge equipment has the capability of updating detection data and remotely updating a data processing model of the edge computing equipment through data cooperative equipment. The data cooperative equipment can receive and manage data of a plurality of test beds, and perform cooperative management on the data, and when the data volume is large and the operation is complex, the data cooperative equipment can call other idle edge computing equipment for processing.
In some embodiments of the present invention, the tested object includes a bearing, the edge device node is Compact RIO, the sensor includes a vibration sensor, a speed sensor and a temperature sensor, and the expert system algorithm includes a bearing diagnostic algorithm.
Specifically, some portable finished vehicles, bogie overhaul equipment and other large-scale equipment (such as a rolling table) adopt mature acquisition equipment at home and abroad, and after edge equipment and data coordination equipment are added, edge calculation results can be provided for owners as equipment additional functions. The invention takes bearing detection equipment as an example, and introduces an edge calculation framework of the equipment.
The bearing detection equipment under the framework can realize the detection of the running state of the equipment or the tested piece in real time by using the bearing fault diagnosis system, and ensure the safe maintenance and the instructive maintenance. When the tested piece is provided with the bearing equipment, the problem that the bearing is not disassembled and assembled for maintenance can be solved.
Compact RIO with high real-time data processing is an edge node which has strong data acquisition, calculation and data storage capacity and consists of a reconfigurable Field Programmable Gate Array (FPGA) case and a real-time controller (RT). The FPGA is responsible for high-speed acquisition of vibration quantity, speed signals and temperature signals, the RT deploys a bearing real-time diagnosis and detection algorithm and communicates with an industrial control system, and the industrial control system displays and uploads a processing result to a cloud server.
The invention provides an edge calculation framework based on a mature acquisition instrument, which realizes edge calculation by utilizing the acquisition and calculation functions of the acquisition instrument. When the acquisition instrument device does not have enough computing power or does not provide an open interface of computing power, the acquisition instrument device can be replaced by a self-developed chassis type embedded device or other acquisition devices. When the data volume is large and the operation is complex, the data cooperation device can call other idle edge computing devices for processing and can also update the data processing model in the edge computing devices.
In some embodiments of the present invention, the non-standard detection device includes an image acquisition device, the edge device node is an image monitoring edge device, and the expert system algorithm is configured to determine a pantograph state, a vehicle running direction, and a vehicle number identification according to the image acquisition data.
Specifically, the edge side can realize the preprocessing of image data, such as image screening, image gray value extraction and the like, reduce the uploading bandwidth of the image, reduce the volume of the equipment to a greater extent and improve the processing rate of the image data of the equipment. By taking image-based trackside pantograph abrasion detection equipment in the rail transit industry as an example, a pantograph slide plate belongs to a consumable part, is replaced periodically, and has high failure probability. The pantograph abrasion detection system monitors the vehicle pantograph by adopting a non-contact measurement technology, and the image processing edge node has the functions of system self-checking, data communication and data management and automatically judges the states of wear, outline, center line offset, cleat deformation and the like of the pantograph passing through the vehicle; and can automatically judge the running direction of the train and automatically identify the train number. The wear of the pantograph and the defects of other parts of the pantograph can be accurately detected.
In the edge nodes of the image system, a GPU is used for processing image data and training models such as deep learning; data can also be sent to other edge node devices or cloud terminals through the data cooperation device to carry out model training; and the CPU in the edge compute node is used for functional logic control and data communication.
Referring to fig. 8 to 10, in the foregoing embodiments, the expert system algorithm may adopt the following execution flow:
a data preprocessing step: the method comprises the steps of data slicing processing, filtering processing, resampling processing and normalized sampling frequency processing;
a characteristic extraction step: determining normal key features and fault key features based on statistical information of the normal operation data and the fault operation data;
an algorithm training step: performing algorithm training based on the training model, the preprocessed data and the feature extraction data;
the algorithm pre-verification step: and comparing the detected data of the tested piece with the generated data of the algorithm to verify the accuracy, the usability and the effect of the algorithm.
In some embodiments of the invention, the expert system algorithm is further configured to:
performing data preprocessing and feature extraction based on historical data;
and performing fault data injection based on twin data, and performing algorithm training by combining the data after feature extraction.
Taking an edge device for a brake system as an example, a high-precision model of the brake system is embedded in the edge device. Monitoring data are implemented through a sensor aiming at the brake system and fed back to non-standard detection equipment aiming at the brake system, and meanwhile, a digital twin body is obtained through the non-standard detection equipment aiming at the brake system.
The expert system is provided with a data storage unit for storing historical operating data, and performs data preprocessing and characteristic quantity selection based on the historical operating data. And perfecting fault characteristic data based on fault data in the twin data. And forming a mature fault diagnosis model based on the training result of the expert system so as to perfect the function of the expert algorithm.
By adopting the method, the non-standard detection equipment of the traditional industrial personal computer can easily deploy the monitoring of the working state of the equipment and the expert system of the tested piece; the safety of the operation of the device (especially for large rolling devices such as vehicle rolling vibration test beds) can be greatly improved, and the additional functional value of the device can be increased.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A non-standard detection device monitoring system is characterized by comprising:
edge equipment: the system comprises a plurality of edge equipment nodes, wherein each edge equipment node can at least communicate with a type of non-standard detection equipment to acquire the running state information and detection data of the type of non-standard detection equipment; each type of non-standard detection equipment corresponds to at least one type of communication interface; the operation state of each non-standard detection device can be analyzed based on the collected operation data, and the data processing of the non-standard devices is assisted based on the detection data of the non-standard devices;
the data cooperation device: communicating with each edge device node, distributing cloud server data, and cooperatively scheduling the computing capacity of the edge device to realize data processing optimization;
the remote gateway: the data coordination equipment is connected with the data coordination equipment, communicates with the cloud server, and sends the control information and the update information of the edge equipment node to the edge equipment node through the data coordination equipment;
the non-standard detection equipment is communicated with the remote gateway and the tested piece so as to send the detected information of the tested piece to the remote gateway and collect the detection data of the tested piece.
2. The monitoring system of claim 1, wherein the data coordination device is configured at an edge device node connected to the non-standard detection device, and when the data coordination device has a new running computation requirement, the data coordination device distributes data required by the new running computation requirement to a free edge device node to perform running computation, so as to realize data processing optimization.
3. The non-standard detection device monitoring system of claim 1, wherein the edge device node is configured with:
and (3) a state monitoring algorithm: acquiring data of non-standard detection equipment, and analyzing the running state of the non-standard detection equipment based on the data;
an expert system algorithm: and acquiring sensor data, and performing data analysis on detection data of the non-standard detection equipment to analyze the performance of the tested piece.
4. The non-standard detection device monitoring system of claim 3, wherein the non-standard detection device comprises an industrial personal computer, the edge device comprises a TCP/IP-based edge device node, the edge device node is in communication with the industrial personal computer and a sensor, and the sensor is connected to a tested piece;
the data required by the state monitoring algorithm of the industrial personal computer and the algorithm of the expert system can be directly acquired by the sensor information, or data twinning is carried out by the industrial personal computer to acquire the data of the industrial personal computer.
5. The non-standard detection device monitoring system of claim 4, wherein the flow of execution of the expert system algorithm comprises:
a data preprocessing step: the method comprises the steps of data slicing processing, filtering processing, resampling processing and normalized sampling frequency processing;
a characteristic extraction step: determining normal key features and fault key features based on statistical information of the normal operation data and the fault operation data;
an algorithm training step: performing algorithm training based on the training model, the preprocessed data and the feature extraction data;
the algorithm pre-verification step: and comparing the detected data of the tested piece with the generated data of the algorithm to verify the accuracy of the algorithm.
6. The non-standard detection device monitoring system of claim 5, wherein the expert system algorithm is further configured to:
performing data preprocessing and feature extraction based on historical data;
and performing fault data injection based on twin data, and performing algorithm training by combining the data after feature extraction.
7. The non-standard inspection device monitoring system of claim 6, wherein the non-standard inspection device comprises an image capture device, and when the edge device node is an image capture edge device, the expert system algorithm is configured to perform image processing algorithms such as image de-noising, image recognition, etc. based on image capture data.
8. The system of claim 1, wherein the edge device further comprises an edge device node based on serial communication modes such as RS485 and RS232, an edge device node based on USB, and an edge device node based on GPIB.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202110917137.3A CN113627527B (en) | 2021-08-11 | 2021-08-11 | Nonstandard detection equipment monitoring system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114217540A (en) * | 2021-11-10 | 2022-03-22 | 深圳市鑫信腾科技股份有限公司 | Control method, device, equipment and storage medium of non-standard equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110830943A (en) * | 2019-11-06 | 2020-02-21 | 湖南银河电气有限公司 | Equipment state monitoring system based on edge calculation and big data analysis |
| CN111698470A (en) * | 2020-06-03 | 2020-09-22 | 河南省民盛安防服务有限公司 | Security video monitoring system based on cloud edge cooperative computing and implementation method thereof |
| CN112463393A (en) * | 2020-12-14 | 2021-03-09 | 国网辽宁省电力有限公司抚顺供电公司 | Power distribution Internet of things edge computing architecture design method based on Mongo cluster technology |
| KR102262321B1 (en) * | 2019-11-29 | 2021-06-08 | 주식회사 두두원 | IoT GATEWAY SYSTEM FOR INDUSTRIAL |
| CN113112086A (en) * | 2021-04-22 | 2021-07-13 | 北京邮电大学 | Intelligent production system based on edge calculation and identification analysis |
-
2021
- 2021-08-11 CN CN202110917137.3A patent/CN113627527B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110830943A (en) * | 2019-11-06 | 2020-02-21 | 湖南银河电气有限公司 | Equipment state monitoring system based on edge calculation and big data analysis |
| KR102262321B1 (en) * | 2019-11-29 | 2021-06-08 | 주식회사 두두원 | IoT GATEWAY SYSTEM FOR INDUSTRIAL |
| CN111698470A (en) * | 2020-06-03 | 2020-09-22 | 河南省民盛安防服务有限公司 | Security video monitoring system based on cloud edge cooperative computing and implementation method thereof |
| CN112463393A (en) * | 2020-12-14 | 2021-03-09 | 国网辽宁省电力有限公司抚顺供电公司 | Power distribution Internet of things edge computing architecture design method based on Mongo cluster technology |
| CN113112086A (en) * | 2021-04-22 | 2021-07-13 | 北京邮电大学 | Intelligent production system based on edge calculation and identification analysis |
Non-Patent Citations (1)
| Title |
|---|
| 李宝超;武智强;张承瑞;胡天亮;: "面向3C非标检测设备的可配置监控系统研究与实现", 组合机床与自动化加工技术, no. 09 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114217540A (en) * | 2021-11-10 | 2022-03-22 | 深圳市鑫信腾科技股份有限公司 | Control method, device, equipment and storage medium of non-standard equipment |
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